{"id":"W2067641808","doi":"10.1021/ie060359h","title":"An Algorithm to Calculate K- and L-Points","year":2006,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Algorithm; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004040004,0.0002020919,0.0002364296,0.0001389236,0.00006450557,0.00008972643,0.0002658094,0.0002328068,0.00005033941],"category_scores_gemma":[0.0001154703,0.0002192293,0.00004007392,0.0007646714,0.00004703967,0.0001304195,0.00008226278,0.0007664646,0.00001672138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797598,"about_ca_system_score_gemma":0.00001630233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009126162,"about_ca_topic_score_gemma":9.584925e-7,"domain_scores_codex":[0.9984018,0.00001854184,0.0002541264,0.0003291862,0.0004071399,0.0005892352],"domain_scores_gemma":[0.9991577,0.00008804605,0.00001064444,0.00035147,0.00008600989,0.0003061825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005120849,0.00002321124,0.00007255079,0.000026525,0.00001962225,0.00002633143,0.00001152571,0.06110125,0.9165402,0.00002033521,0.001226575,0.02092676],"study_design_scores_gemma":[0.000236559,0.0000342691,0.0001067361,0.0000420138,0.000006749582,0.00001116132,0.00001092741,0.1509857,0.8315505,0.0001620747,0.01654696,0.0003063461],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9627188,0.0002477035,0.03285218,0.00020346,0.0001146895,0.0004129077,0.00004139537,0.001902391,0.001506434],"genre_scores_gemma":[0.9924076,0.00001555909,0.006183561,0.000005137207,0.0009550308,0.00007729428,0.00003075451,0.00008206649,0.0002429864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08988445,"threshold_uncertainty_score":0.8939912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03387377471138055,"score_gpt":0.3247515388329645,"score_spread":0.290877764121584,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}